IBM gathers statistics related to the five 6 R’s on 5000 universities world wide… The best relations between IBM and universities involve what we call the five R’s – Research (or open collaborative research with a focus on grand challenge problems for business and society), Readiness (or skills), Recruiting (or jobs working on teams to building a smarter planet), Revenue (which is more and more about public-private partnerships that connect great universities and great cities), Responsibility (where IBM employees share their expertise, time, and resources with universities – including IBM guest lecturing in courses or judging student competitions), and Regions – newest and most important working with regional innovation ecosystems, in conjunction with our IBM Global Entrepreneurs program and SmartCamps…. About 15-20% of awards are in the analytics areas, and we see that growing to 25-33% this coming year and the future…. For more information: http://www.ibm.com/university Bay Area numbers… 300 fulltime hires in last five years 400 interns and co-ops students over 1000 employees who are alumni, between 2-10% executives over $3M in research and matching grant awards, over five times that in matching from government good customers of IBM

What improves quality of life? Service system innovations. Every day we are customers of 13 types of service systems. If any of them fail, we have a “bad day” (Katrina New Orleans) I have been to two service science related conferences recently, one in Japan on Service Design and one in Portugal on Service Marketing… the papers from the proceedings of the conferences mapped onto all of these types of service systems… The numbers in yellow: 61 papers Service Design (Japan) / 75 papers Service Marketing (Portugal) / 78 Papers Service-Oriented Computing (US) Number in yellow Fist number: Service Design Conference, Japan 2 nd International Service Innovation Design Conference (ISIDC 2010), Future University Hakodate, Japan Second number Service Marketing Conference, Portugal, AMA SERVSIG at U Porto, Portugal Numbers in yellow: Number of AMA ServSIG 2010 abstracts that study each type of service system… (http://www.servsig2010.org/) Of 132 total abstracts… 10 studies all types of service systems 19 could not be classified In a moment we will look at definitions of quality of life, but for the moment, consider that everyday we all depend on 13 systems to have a relatively high quality of life, and if any one of these systems goes out or stops providing good service, then our quality of life suffers…. Transportation, Water, Food, Energy, Information, Buildings, Retail, Banking &amp; Financial Services (like credit cards), Healthcare, Education, and Government at the City, State, and National levels…. Volcanic ash, hurricanes, earthquakes, snow storms, floods are some of the types of natural disasters that impact the operation of these service systems – but human made challenges like budget crises, bank failures, terrorism, wars, etc. can also impact the operation of these 13 all important service systems. Moreover, even when these systems are operating normally – we humans may not be satisfied with the quality of service or the quality of jobs in these systems. We want both the quality of service and the quality of jobs in these systems to get better year over year, ideally, but sometimes, like healthcare and education, the cost of maintaining existing quality levels seems to be a challenge as costs continue to rise… why is that “smarter” or sustainable innovation, which continuously reduces waste, and expands the capabilities of these systems is so hard to achieve? Can we truly achieve smarter systems and modern service? A number of organizations are asking these questions – and before looking at how these questions are being formalized into grand challenge questions for society – let’s look at what an IBM report concluded after surveying about 400 economists…. ==================== Quality of life for the average citizen (voter) depends on the quality of service and quality of jobs in 13 basic systems….. Local progress (from the perspective of the average citizen or voter) can be defined for our purposes as (quality of service &amp; jobs) + returns (the provider, which is really the investor perspective, the risk taker in provisioning the service) + security (the authority or government perspective on the cost of maintaining order, and dealing with rules and rule violations) + smarter (or the first derivative – does all this get better over time – parents often talk about wanting to help create a better world for their children - sustainable innovation, means reducing waste, being good stewards of the planet, and expanding our capabilities to do things better and respond to challenges and outlier events better)…. Without putting too fine a point on it, most of the really important grand challenges in business and society relate to improving quality of life. Quality of life is a function of both quality of service from systems and quality of opportunities (or jobs) in systems. We have identified 13 systems that fit into three major categories – systems that focus on basic things people need, systems that focus on people’s activities and development, and systems that focus on governing. IBM’s Institute for Business Value has identified a $4 trillion challenge that can be addressed by using a system of systems approach. Employment data… 2008 http://www.bls.gov/news.release/ecopro.t02.htm A. 3+0.4+0.5+8.9+1.4+2.0=16.2 B. C.13.1+1.8=14.9 Total 150,932 (100%) Transportation (Transportation and Warehousing 4,505 (3%)) Water &amp; Waste (Utilities 560 (0.4%)) Food &amp; Manufacturing (Mining 717 (0.5%), Manufacturing 13,431 (8.9%), Agriculture, Forestry, Fishing 2,098 (1.4%)) Energy &amp; Electricity Information (Information 2,997 (2%)) Construction (Construction 7,215 (4.8%)) Retail &amp; Hospitality (Wholesale Trade 5,964 (4.0%), Retail Trade 15,356 (10.2%), Leisure and hospitality 13,459 (8.9%)) Financial &amp; Banking/Business &amp; Consulting (Financial activities 8,146 (5.4%), Professional and business services 17,778 (11.8%), Other services 6,333 (4.2%)) Healthcare (Healthcare and social assistance 15,819 (10.5%) Education (Educational services 3,037 (2%), Self-employed and unpaid family 9,313 (6.2%), Secondary jobs self-employed and unpaid family 1,524 (1.0%)) City Gov State Gov (State and local government 19,735 (13.1%)) Federal Gov (Federal government 2,764 (1.8%))

SUR Awards South China Univ of Tech/ Xi&apos;an Jiaotong (China) : Z-platform, very large scale electricity usage for a/c during summer National Grid and Ministry of Water shared platform for large scale hydrological modeling and parallel computing Universidade de São Paulo,Universidade de Mato Grosso (Brazil): Great Rivers Project: Modeling river basins in Brazil and developing a system for management of rivers University of Wisconsin-Madison/U de A São Paulo / U Federal de Mato Grosso (Brazil) – Water for Tomorrow; Developing a Sustainable Water Management System Fudan / North China Electric Power / Shanghai Jiaotong (China) – Smarter Planet China projects Financial services risk management Maintenance scheduling for electricity distribution networks, Dynamic, distributed framework to enable non-IBM applications can run on IBM Cloud hardware/software University of Calgary (Canada): Advanced petroleum reservoir modeling &amp; green technologies Tekniske Universitet (Denmark) : Creation of Powerlab on Intelligent Utility Networks Wageningen Universiteit, Universiteit Twente, Erasmus Universiteit Rotterdam, Technische Universiteit Delft, Rijksuniversiteit Groningen and Vrije Universiteit Amsterdam (Netherlands): significantly reduce waste of fresh food in the chain, by 40 %. Each year waste 1B euros of fresh produce, 1/3 of the fresh food grown and produced. Mc Master University (Canada) - Next Gen Autos, Using Multi-Core Processors Univ of Maryland Baltimore County - Real-time wildfire and smoke monitoring and prediction analysis using multi-channel data assimilation and System S/InfoSphere Streams Fraunhofer-Gesellschaft (Germany) - POC for Integration of IBM Idemix into new German Electronic ID-Cards and Identity Management Infrastructure Univ of Calf-Berkeley (US) - CA Energy Innovation Center: Smart Energy Technologies and Management Ozyegin University (Turkey) – Cloud computing research center to create “smart” applications for various sectors University of Transilvania of Brasov (Romania) - This project is to conceive and design an intelligent distributed data acquisition and control architecture based on models, wireless sensors and actuator, access to renewable energy sources, and computing networks Tsinghua, Fudan, Peking Universities (China) - Carbon Management across the Railroad Industry, Detecting Risk in Food Chains, Monitoring and Detecting Outbreaks in Large Water Distribution Systems Stanford University (US) - This collaboration with Stanford&apos;s Smart Fields group will focus on optimizing the management and production from oil wells and business analytics. Optimizing well management has the potential of increasing the recovery of petroleum resources University of Ottawa (Canada) - to support research collaborations in four areas -- innovation and entrepreneurship, health care management, global business and international management, and performance management. Engaged with Telfer and the Ottawa Hospital Research Institute (also affiliated with the U of O) in BIPM research designed to significantly reduce &amp;quot;adverse events&amp;quot; in hospitals Royal Inst. Of Technology (Sweden) – to support collaboration with the Intelligent Traffic Systems (ITS) Lab at the university is to create a computing infrastructure to build smarter systems to manage transport operations in urban areas by integrating data from multiple sources Nat’l Inst.of Disaster Prevention (Korea), Beijing Normal / BJ Jiaotong / U of Fin &amp; Econ ( China/Korea) - collaborations will help research team quickly gain access to city emergency management issues and data, to build assets. Performance Analysis and Optimization for Typhoon Emergency Response. Analyze and optimize emergency responses to railroad disasters. VTT, Technical Research Center of Finland (Finland) - collaboration aimed at understanding the use of social networks (e.g., communities of bus and taxi drivers and users) for collecting new data, analyzing traffic data and developing new applications, and disseminating traffic information. Univ of Colorado-Boulder (USA) - to i mprove the technology interface between the homeowner and the utility, through the use of Personas/Fictional Representatives. The goal is to improve acceptance and make the smart grid more usable by the homeowner, and design a better user interface. Improve the efficiency of a smart grid using simulation optimization modeling and queueing theory Indian Institute of Technology-Roorkee (India) - Development of data mining algorithms for analyzing time series data and applying them to real case data such as the meteorological data from the Indian peninsula and earth science data for understanding the behavior of earth’s eco-system, detecting patterns in changes in the forest cover in India, and prediction of natural disasters. University of Jyvaskyla (Findland) - University of Jyväskylä and its partners (Ministry of Finance, IBM, others) have developed a Finnish Enterprise Architecture (FEAR) governance and assurance model; the goal is to provide an architecture so when designing and building applications, they can be easily integrated across multiple systems and silos. Inha University (Korea) - This SSME project with the Inha University Service Science Center aims to identify which healthcare service processes customers value. Based on these analyses, a prototype model for service evaluation will be developed to identify the strength and weakness in a provider&apos;s core processes, activities, resources. The goal is to create a model to recommend required processes, activities and resources to focus on to improve customer service quality and loyalty in service industries. Tsinghua / U of Shanghai for S&amp;T / Beijing U of Posts &amp; Telecom / Peking Union Medical / Xi&apos;an Jiaotong Univ (China) - IBM and Tongji University (ToJU) signed a Smarter City Initiative collaboration agreement aimed at building and providing integrated IBM Smarter City solutions in China. The goal of this collaboration will be to overcome the current silo decision making by different government ministries and departments and to provide a city mayor and other decision makers at the city level an integrated Smarter City framework, solution package, and a real city model. Carnegie Mellon University (USA) - ee IBM has signed an MOU with CMU to create an IBM Smarter Infrastructure lab. This lab will initially take a system of systems view of a university managed like a smart city using sensors, data, and analytics. There are several goals for this collaboration. 1. Design and develop fixed and mobile infrastructure analytics technologies and solutions for a smarter planet (e.g. smart water, waste, buildings, energy, transportation, healthcare, environment, etc.). 2. Provide a showcase for client visits and demonstrations of IBM Smarter Cities technologies, including iDataPlex and software -- SPSS, Infosphere Streams, Maximo, Cognos, and Cisco Telepresence. Erasmus University Rotterdam (Netherlands) - This collaboration with the Erasmus Center for Optimization in Public Transportation (ECOPT) and the Netherlands Railway will focus on managing major failures in the railway system, such as track breaks, signal or switch failures. The team will develop an analytic framework/solution to coordinate and optimize the rescheduling of timetables, trains, and crew after a major disruption; and build a POC based on data from the Netherlands Railway. This project will be based on iLog and support from IBM Global Rail Innovation Center in China. Tokyo Institute of Technology (Japan) - This collaboration is to explore providing InfoSphere Streams services hosted at IBM and to build a computing platform based on InfoSphere Streams that will handle massive amounts of data produced in two areas. 1. from sensors on cell phones, to monitor real time traffic and the environment – e.g., CO2 levels. 2. from next-generation DNA sequencers. University of Toronto / University or Waterloo (Canada) - The focus of this collaboration is to build a research platform to model the Ontario Grand River Basin in Eastern Ontario. Eight Ontario research universities, including Waterloo and Toronto, the regional municipalities, and 80+ private sector companies are establishing a $25+M regional platform in the Grand River water basin that will provide infrastructure to: do research on the management of water in urban and urbanizing watersheds; to be a test-bed for new technologies and services; and to inform public policy in Ontario and new legislation from the Provincial Government Syracuse University (USA) - This SUR collaboration is to develop a system based on Tivoli tools to monitor and operate a green data center of IT and non-IT (includes micro-turbines and chillers) equipment and to optimize its use of energy in the data center. A key objective will be for IBM and its partners to develop turn key solutions for additional advanced green data centers. Aalto University (Finland) - Finland has been chosen as the site for many data centers, including Google and the Finnish IT services &amp; consulting company. Currently in Finland there is little in-depth understanding of how data centers should be built and run following green principles – including the best practices in terms of energy consumption and reuse of the heat generated by the hardware, and the use of renewable and green energy sources. The goal of DC2F is to 1. review the current state of the art technologies in setting up and running green DCs, 2. experiment and verify key technologies to see if the promise they make are truly what they claim to be, and 3. develop new ways to lower the total effect of a DC on our environment and energy usage. North China Energy &amp; Power / Harbin Inst / Zhejiang / Huazhong U of Sci and Tech (China) - IBM and Tongji University (ToJU) signed a Smarter City Initiative collaboration agreement aimed at building and providing integrated IBM Smarter City solutions in China. The goal of this collaboration will be to overcome the current silo decision making by different government ministries and departments and to provide a city mayor and other decision makers at the city level an integrated Smarter City framework, solution package, and a real city model. Egypt Japan University of Sci &amp; Tech (EJUST) (Egypt) - This collaboration is a POC of an HPC cloud built with our HPC stack on top of our CloudBurst stack using both x and p blades. An initial project will be to develop multi-agent traffic simulation models for Alexandria city and its suburb Burg El Arab, helping to improve traffic flow jams in Alexandria. Dublin City (Ireland) - This collaboration between IBM Research’s newly established Smarter Cities Technology Center in Ireland and Dublin City U&apos;s Centre for Digital Video Processing (CDVP) is to develop a platform using IBM HW and Infosphere Streams to combine the enormous amounts of data from city sensors on transport, air quality, energy &amp; water-usage and others. Slovak University of Technology (Slovakia) - This collaboration is to develop telemedicine solutions based on sensors and wireless technology. Examples include monitoring people who have just been discharged from hospitals, elderly people living at home, and people with chronic illnesses IIT Karagpur (India) - This collaboration will focus on the use of data analytics and mining, simulation, modeling, and optimization, in two smarter planet topics ; Water management of the Ganges river basin and telemedicine and medical informatics. In addition the collaborators will study how to create an integrated platform of computing, storage, simulation and analytics tools to support computationally intensive application areas OCR Green Supply Chain Simulator (China) - IBM CRL and Tsignhua University to create a a software Carbon Effective Supply Chain Simulator (CESCS), optimal balance between carbon footprint , service level, supply chain cost and product quality. Interaction Network Analysis for Service Delivery (India ) - IBM IRL and the Indian School of Business to develop software that models, simulates, and optimizes service delivery social interaction networks. New Gen Hospital (Israel) - collaboration with IBM HRL, Technion University and Rambam Hospital for Lean and Safe hospital SW for the Aging Population (US/Scotland) - collaboration with IBM WRL and the University of Dundee and University of Miami School of Medicine to; Online learning methods for both younger and older workers Examine physical and cognitive changes with age as they impact workers Determine how best to integrate accessibility enhancements for aging into standard software (Web, IM, mail) Medical Image Analysis (US) - with IBM WRL and Yale University on novel algorithms for medical image and video analysis for cardiac Magnetic Resonance Imaging (MRI) and cardiac Computed Tomography (CT) images, and attempt to develop algorithms for segmentation, tracking and pattern analysis exploiting medical knowledge to distinguish between different cardiac diseases. Managing Business Integrity (US) - IBM and NYU Stern School of Business to build a risk assessment model that can be used by executive decision makers to make cost/benefit decisions about the corporate governance of customer data. Managing Business Integrity (US) - IBM and University of Illinois to develop techniques to analyze security risks in business processes and to determine the impact of security investments and countermeasures to reduce security risks. This will involve business process and IT infrastructure instrumentation and modeling, as well as modeling of the business process and IT infrastructure and countermeasures. Open Advancement of Question Answering (US) - IBM and University of Massachusetts to develop OAQA technologies and conduct an open, transparent evaluation of these technologies to facilitate the rapid advancement of the question answering field. Open Advancement of Question Answering (US) - IBM and University of Southern California to develop OAQA architecture, data model, and component technologies and collaborate for the rapid advancement of the question answering field. Open Advancement of Question Answering (US) - IBM and University of Texas to develop OAQA architecture, data model, and component technologies and collaborate for the rapid advancement of the question answering field. Open Advancement of Question Answering (US) - IBM and Carnegie Mellon University to develop OAQA architecture, data model, and component technologies and collaborate for the rapid advancement of the question answering field. Architectural &amp; Social Governance of SW development (US) - IBM and Carnegie Mellon University to explore use of a common set of analytic techniques for exploring key structural and behavioral properties of architectures and the teams supporting them Patterns of Interest in Financial Data Streams (US) - IBM and University of Albany to develop models and methodologies for specifying and detecting patterns of interest in financial data streams and evaluate the resulting techniques using IBM’s stream processing middleware platform Reputation of Internal IP Address (US) - IBM and Polytechnic Institute of NYU will perform research in the area of cyber security and in particular, the reputation of IP addresses Architectural &amp; Social Governance of SW development (US) - IBM and University of Virginia to explore use of a common set of analytic techniques for exploring key structural and behavioral properties of architectures and the teams supporting them Radical Simplification of Artifact-Centric Business Process Modeling (Italy) – IBM and University of Rome La Sapienza to perform fundemental research on foundational aspects of the artifcat-centric approach to the management and execution of business processess and operations with a focus on the relationship of ontologies to the artifact-centric approach. Radical Simplification of Artifact-Centric Business Process Modeling (US) – IBM and University of California Santa Barbara to perform fundemental research on foundational aspects of the artifact-centric approach to the management and execution of business processes and operations. Radical Simplification of Artifact-Centric Business Process Modeling (US) – IBM and University of California San Diego to perform fundemental research on foundational aspects of the artifact-centric approach to the management and execution of business processes and operations. Development open source software for design in inverse problems (Canada) – IBM and University of British Columbia to explore more advanced multi-level optimization approaches for desing of software to address inverse problems with empahsis on large scale problems. Development of innovative algorithms for reservoir management &amp; production optimization (Norway) – IBM and Norwegin University of Science &amp; Technology to develop methods for real-time production optimization suitable for large systems of wells and pipelines that include discrete decision variables. Development of intelligent emergency response systems (Portugal) – IBM and Carnegie Mellon Portugal / Madeira Interactive Technologies Institute to explore enabling intelligent emergency response coordination during natural disasters and large events within an effected georgaphioc area. Clinical Genomic Analytics (Israel) – IBM and Tel Aviv University to collaborate on IBM’s BDM Tool for bio-clinical data mining. This is based on a framework that allows combining building blocks that perform various functions into a workflow that performs a high level task, feature selection etc. Open Advancement of Question Answering (Italy) - IBM and Università degli Studi di Trento to explore algorithums in the area of relation extraxction patterns and features generaliztion to apply these to question classification in Jeopardy categories and evaluate the resulting performance. Crowd Computing (Israel) – IBM and Haifa University to collaborate to develop a general crowd computing platform to be used to develop crowd computing applications Virtual Cyber-security Lab (USA) – IBM and Georgia Tech to collaborate to create open test bed for developing, testing , benchmarking and validating new and innovative network defense technologies for cybersecurity Multi-modal service platform ( Inida ) – IBM and IIT Bombay to collaborate to derive an interface on mobile devices that can be used by people on the other side of the digital divide to access information technology solutions. Deep Q&amp;A medical domain (USA) – IBM and Columbia University to collaborate to create a prototype system that leverages large components of the DeepQA system to provide relevance-ranked passages that answer questions posed by medical professionals. Development of dynamic state estimators using Phasor Measurement Unit ( India ) – IBM and IIT Madras to design a reiable and survivable network for inconnecting synchrophasors and enterprise systems. Development of dynamic state estimators using Phasor Measurement Unit ( India) – IBM and IIT Kharagpur to develop dynamic state estimators unsing Phasor Measurement Unit (PMU). Peta-Scale Data Workflow ( Saudi Arabia) – IBM and King Abdullah University of Science (KAUST) to build a scalable data management layer that is capable of managing petabytes of data efficiently and supporting real-time decision making Faculty Awards Rochester Institute of Technology Organometallic Vapor Phase Epitaxy of III-V Photovoltaic Materials on Novel Substrates Sabanci University Green Logistics: Minimizing Greenhouse Gas Emissions in Road Transport University at Buffalo, State University of New York Reducing Data Center Operational Costs by Implementing Green Technologies Middle East Technical University ENERGY-EFFICIENT WIRELESS MOBILE NETWORKING TECHNOLOGIES TO ENABLE SMART INFRASTRUCTURES Institute of Photonic Sciences Transparent conducting electrodes for silicon solar cells Delft University of Technology How to make urban water management smart: Connecting measurements, models and management. UNIVERSITY OF DODOMA E-SYSTEM FOR SMALL AND MEDIUM SCALE ENTERPRISE (E-COMMERCE) Massachusetts Institute of Technology Center for Urban Services Research, Education and Entrepreneurship Stanford University Algorithms and Incentives for Societal Networks: Smarter Tourism, Transportation and Energy Systems San Jose State University SSME Curriculum and Research Program at San Jose State University Royal Melbourne Institute of Technology Study of Readiness of University of Dodoma in Adopting and Embracing Complex New System of Systems University of Geneva Smart City emerging from Services Science Ohio State University Towards Smarter Socio-Technical Systems via Content and Network Analysis University of Illinois at Urbana-Champaign Assessing, Documenting, and Disseminating a Scalable Change Incubator for the Transformation of Engineering and Computer Science Education University of Southern California Smarter Cities: A Distributed Optimization &amp; Control Framework for Smart Energy Networks Bilkent University Network Middleware for Environmental Monitoring and Control with Wireless Ad hoc, Mesh and Sensor Networks Clemson University Performance Modeling of Data Staging in Storage Hierarchies Carnegie Mellon University Exploratory research in smart infrastructure including sensing and preventive response of facilities such as buildings, and sewer pipes. Carnegie Mellon University Exploratory research in smart infrastructure including sensing and preventive response of facilities such as buildings, and sewer pipes. Marist College Dynamic Infrastructure: Reducing costs, improving service, and managing risk in the 21st century Rochester Institute of Technology III-V Solar Cells University of Colorado at Boulder Advances in Rogue Access Point Detection University of Ottawa Self-Organizing Autonomic Computing as Applied to Peer to Peer Server Virtualization University of Toronto Towards Automating Pattern-Based Application Integration Technical University of Denmark Real Time Digital Simulation of Active Distribution Systems Hong Kong University of Science and Technology Text Glyph, Text Bicycle, and Text Splash: A Visual Analytics System for Text documents Tokyo Metropolitan University Advancing computational chemistry as a powerful partner with macro simulations and experimental studies Plekhanov Russian Academy of Economics Distributed AI for Smart Power and Supply Chains Ozyegin University Top Faculty Contributor - IBM Smarter Planet University Jam University of Warwick Turning art into science when protecting our BITs; a framework for understanding how IT controls help limit the risk of data losses Carnegie Mellon University Exploratory research in facilities management with focus on hospital and building information and infrastructure management. Prof. Akinci has experience in creating information models for buildings and is working with a VA hospital Carnegie Mellon University Exploratory research in smart water infrastructure including drinking water in the context of the three rivers in Pennsylvania. Carnegie Mellon University Exploratory research in facilities management with focus on sewer infrastructure. Duke University Integrating Computer Science and Engineering into K-12 ViaTeacher Workshops and the Virtual Computing Laboratory Duke University Toward a Smarter Edge for a Smarter Grid Harvard University Prototyping the Smart City: Environmental and Energy Monitoring with an Urban-Scale Sensor Network New York University work in the area of cyber security, in particular, the reputation of user identities North Carolina State University Open Source Health Care and the Virtual Computing Laboratory North Carolina State University Evaluation of the VCL in High School Classrooms in North Carolina – Phase 2 North Carolina State University Virtual Collaboration Environments: Socio-Technical Resource Allocation for Scalable Performance of the Collaborative Web North Carolina State University Virtual Collaboration Environments: Socio-Technical Resource Allocation for Scalable Performance of the Collaborative Web Lehigh University Assessing Fiber-Matrix Strength in Organic Substrates University of Connecticut UConn-Ethiopian Academic Partnership in Sustainable Water Resources University of California-Riverside Narrow Bandgap Semiconductor Materials and Devices for Thermoelectrics and Spintronics University of Roma La Sapienza The social dimension of (secure) ad-hoc networks Georgia Institute of Technology Evaluating Applications of Streaming Workloads on Multicore Technology Ecole Supérieure Multinationale des Télécommunications A Laboratory for Mobile Technology for Development at ESMT New York University Smarter City Research Innovation Center City University of New York City College Smarter City Research Innovation Center Indian Institute of Management, Bangalore Innovative delivery models University of Massachusetts Curriculum Enhancement with IBM Technologies: Business Intelligence/WebSphere Commerce/Business Process Modeling/Social/Networking/Mashu Princeton University High Efficiency Heterojunction Solar Cells National Chiao Tung University Investigating Healthcare Applications on JoinMe platform and Cloud System Stanford University Smart University Management System Università degli Studi di Napoli - Parthenope Smarter People on the Planet University of Colorado Health Sciences Center Imagine! SmartHomes University of California-Berkeley IBM Smarter Planet University Jam -- Top Faculty Contributor Honorable Mention Stanford University CELL DETECTION AND TRACKING USING A NOVEL BIOCOMPATIBLE AND BIODEGRADABLE NANOPARTICLE IMAGING AGENT University of Minnesota-Twin Cities Smarter services analytics through cross domain Spatio-temporal data mining National University of Rwanda ICT for the enhancement of revenue for the organized co-opertaives through SpokenWeb Technology. Carnegie Mellon University Policy Support for Road User Charging Carnegie Mellon University Exploration of real-time data-mining, data-processing, social networking &amp; service composition in mobile 311 systems North Carolina Central University VOICE: Virtualization Outreach Increasing Community Equity North Carolina Central University Virtualization - Increasing Careers Technology Outreach Re-energize Youth (VICTORY) North Carolina State University Analysis and Cost Benefit Assessment of Implementing VCL Cloud Computing Solutions in K-20 Education Ohio State University Main Campus Bayesian Spatio-Temporal Analysis of Very Large Datasets University of Texas at Austin Novel Nanostructured Membranes for Water Purification Florida International University Data Mining and Machine Learning for Green Data Center University of Massachusetts RiverNet: Sensing, Networking and Modeling of Data from a River Sensor Network University of Pittsburgh, Pittsburgh Campus Algorithms for Integrated Scheduling and Power Management Virginia Polytechnic Institute Generalized Sequence Search in Green Computing Environments University of Texas at Austin Micorarchitecture/Software Support in an Evolving World of Programs Written in Managed Languages Running on Multi-core Microprocessors University of Texas at Austin Performance and Power Modeling of multi-core systems using synthetic traces University of California-Berkeley Intgerated Voltage Conversion for High-Performance Digital ICs Indian Institute of Science - IISc Bangalore Storage-class Memories, Transactional Memory and Energy-efficient Storage Applications Ben-Gurion University Combining flash drives and de-duplication technologies, for greener, better performing and affordable storage. Carnegie Mellon University Power Management in IBM BladeCenter Clusters Duke University Energy Isolation for Virtual Computing North Carolina State University A prototype power-aware cloud computing framework State University of New York at Stony Brook The Impact of Storage Software and Aging on Power Consumption National University of Singapore Fabrication, Characterization &amp; Performance of Thin Film Si Photovoltaic (PV) Cells. North Carolina State University Enabling Heterogeneity for Power-Friendly Performance University of Arizona Broadband Characterization Techniques for Product-Level Printed Circuit Boards Georgia Institute of Technology Energy Efficient Thermal Management of High Power Data Centers Via Physics Based Efficient Computational Modeling National Technical University &apos;Kharkiv Polytechnic Institute&apos; Design and Development of Data and Information Infrastructure Solution for University of Wisconsin-Madison Green Supply Chain Management and Analytics University of Tokyo Micro and Extended-Nano Fluidic Systems on Chip and Application to IT-linked Chemistry and Biotechnology

Why service scientists are interested in universities…. They are in many ways the service system of most central importance to other service systems… http://www.upload-it.fr/files/1513639149/graph.html Graph based on data from Source: http://www.arwu.org/ARWUAnalysis2009.jsp Analysis: Antonio Fischetto and Giovanna Lella (URome, Italy) students visiting IBM Almaden Dynamic graphy based on Swiss students work: http://www.upload-it.fr/files/1513639149/graph.html US is still “off the chart” – China projected to be “off the chart” in less than 10 years: US % of WW Top-Ranked Universities: 30,3 % US % of WW GDP: 23,3 % Correlating Nation’s (2004) % of WW GDP to % of WW Top-Ranked Universities US is literally “off the chart” – but including US make high correlation even higher: US % of WW Top-Ranked Universities: 33,865 % US % of WW GDP: 28,365 %

http://www.bls.gov/emp/ep_chart_001.htm http://theeconomiccollapseblog.com/archives/student-loan-debt-hell-21-statistics-that-will-make-you-think-twice-about-going-to-college Posted below are 21 statistics about college tuition, student loan debt and the quality of college education in the United States.... #1 Since 1978, the cost of college tuition in the United States has gone up by over 900 percent . #2 In 2010, the average college graduate had accumulated approximately $25,000 in student loan debt by graduation day. #3 Approximately two-thirds of all college students graduate with student loans . #4 Americans have accumulated well over $900 billion in student loan debt. That figure is higher than the total amount of credit card debt in the United States. #5 The typical U.S. college student spends less than 30 hours a week on academics. #6 According to very extensive research detailed in a new book entitled &amp;quot;Academically Adrift: Limited Learning on College Campuses&amp;quot;, 45 percent of U.S. college students exhibit &amp;quot;no significant gains in learning&amp;quot; after two years in college. #7 Today, college students spend approximately 50% less time studying than U.S. college students did just a few decades ago. #8 35% of U.S. college students spend 5 hours or less studying per week. #9 50% of U.S. college students have never taken a class where they had to write more than 20 pages. #10 32% of U.S. college students have never taken a class where they had to read more than 40 pages in a week. #11 U.S. college students spend 24% of their time sleeping, 51% of their time socializing and 7% of their time studying. #12 Federal statistics reveal that only 36 percent of the full-time students who began college in 2001 received a bachelor&apos;s degree within four years. #13 Nearly half of all the graduate science students enrolled at colleges and universities in the United States are foreigners. #14 According to the Economic Policy Institute, the unemployment rate for college graduates younger than 25 years old was 9.3 percent in 2010. #15 One-third of all college graduates end up taking jobs that don&apos;t even require college degrees. #16 In the United States today, over 18,000 parking lot attendants have college degrees. #17 In the United States today, 317,000 waiters and waitresses have college degrees. #18 In the United States today, approximately 365,000 cashiers have college degrees. #19 In the United States today, 24.5 percent of all retail salespersons have a college degree. #20 Once they get out into the &amp;quot;real world&amp;quot;, 70% of college graduates wish that they had spent more time preparing for the &amp;quot;real world&amp;quot; while they were still in school. #21 Approximately 14 percent of all students that graduate with student loan debt end up defaulting within 3 years of making their first student loan payment. http://www.citytowninfo.com/career-and-education-news/articles/georgetown-university-study-shows-a-bachelors-degree-in-stem-pays-off-11102002 About 65 percent of individuals with bachelor&apos;s degrees in STEM subjects commanded greater salaries than those with master&apos;s degrees in non-STEM fields, according to a Georgetown press release. Likewise, 47 percent of college graduates with bachelor&apos;s degrees in STEM fields earn higher wages than those with doctoral degrees in non-STEM subjects.

Edu-Impact.Com: Growing Importance of Universities with Large, Growing Endowments Recently visited Yang building at Stanford One of the greenest buildings on the planet But if it does not evolve in 20 years it will not be the greenest building Visited supercomputers – we have two at IBM Almaden – there was a time they were in the top 100 supercomputers in the world – not any more …. So a Moore’s law of buildings is more than cutting waste in half every year, it is also about the amount of time it takes to structural replace the material with newer and more modern materials that provide benefits…

T-shaped people are ready for Teamwork – they are excellent communicators, with real world experience, and deep (or specialized) in at least one culture, one discipline and one systems area, but with good team work skills interacting with others who are deep in other cultures, disciplines and systems areas. Also, T-shaped professionals also make excellent entrepreneurs, able to innovate with others to create new technology, business, and societal innovations. T-shaped people are adaptive innovators, and well prepared for life-long learning in case they need to become deep in some new area… they are better prepared than I-shaped people, who lack the breadth. Therefore, IBM and other public and private organizations are looking to hire more of this new kind of skills and experience profile – one that is both broad and deep.. These organizations have been collaborating with universities around the world to establish a new area of study known as service science, management, engineering, and design (SSMED) – to prepare computer scientists, MBAs, industrial engineers, operations research, management of information systems, systems engineers, and students of many other discipline areas – to understand better how to work on multidisciplinary teams and attack the grand challenge problems associated with improving service systems…

There are many opportunities for educational institutions to specialize. Better tuned competence of individuals allows graduates to hit the ground running and better fill roles in business and societal institutions…. Better general education will allow more rapid learning of an arbitrary area of specialization, and create a more flexible labor force… All service systems transform something – perhaps the location, availability, and configuration of materials (flow of things), or perhaps people and what they do (people’s activities), or perhaps the rules of the game, constraints and consequences (governance). How to visualize service science? The systems-disciplines matrix… SSMED or service science, for short, provides a transdisciplinary framework for organizing student learning around 13 systems areas and 13 specialized academic discipline areas. We have already discussed the 13 systems areas, and the three groups (flows, human activity, and governing)… the discipline areas are organized into four areas that deal with stakeholders, resources, change, and value creation. If we have time, I have included some back-up slides that describes service science in the next level of detail. However, to understand the transdisciplinary framework, one just needs to appreciate that discipline areas such as marketing, operations, public policy, strategy, psychology, industrial engineering, computer science, organizational science, economics, statistics, and others can be applied to any of the 13 types of systems. Service science provides a transdisciplinary framework to organize problem sets and exercises that help students in any of these disciplines become better T-shaped professionals, and ready for teamwork on multidisciplinary teams working to improve any type of service system. As existing disciplines graduate more students who are T-shaped, and have exposure to service science, the world becomes better prepared to solve grand challenge problems and create smarter systems that deliver modern service. Especially, where students have had the opportunity to work as part of an urban innovation center that links their university with real-world problems in their urban environment – they will have important experiences to help them contribute to solving grand challenge problems. ================================================ SSMED (Service Science, Management, Engineering and Design) Systems change over their life cycle… what is inside become outside and vice versa In the course of the lifecycle… systems are merged and divested (fusion and fission) systems are insourced and outsourced (leased/contracted relations) systems are input and output (owner ship relations) SSMED standard should ensure people know 13 systems and 13 disciplines/professions (the key is knowing them all to the right level to be able to communicate and problem-solve effectively) Multidisciplinary teams – solve problems that require discipline knowledge Interdisciplinary teams – solve harder problems, because they create new knowledge in between disciplines Transdisciplinary teams – solve very hard problems, because the people know discipline and system knowledge Ross Dawson says “Collaboration drives everything” in his talk about the future of universities… https://deimos.apple.com/WebObjects/Core.woa/BrowsePrivately/griffith.edu.au.3684852440

Service system entities learn to systematically exploit info &amp; tech Learning Systems – Choice and Change Do = operate in comfort zone, applying existing knowledge Copy = to be the best, learn from the rest Invent = double monetize from internal use and external sales Add Rickets “Reaching the Goal” for Internal-External-Interaction Constraints. Explain Incremental-Radical-Super-Radical in terms of units (scientific measurement) For more on Exploitation-Exploration see below.. http://sonic.northwestern.edu/wp-content/uploads/2011/03/Keynote-Watts_Collective_Problems.pdf Lavie D &amp; L Rosenkopf (2006) BALANCING EXPLORATION AND EXPLOITATION IN ALLIANCE FORMATION, The Academy of Management Journal, 49(4). 797-818. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.123.8271&amp;rep=rep1&amp;type=pdf “ Pressures for exploration. Whereas inertia drives firms’ tendencies to exploit, absorptive capacity facilitates counter pressures by furnishing the mechanism via which firms can identify the need for and direction of exploratory activities. Exploration is guided not only by inventing but also by learning from others (Huber, 1991; Levitt &amp; March, 1988) and by employing external knowledge (March &amp; Simon, 1958). Absorptive capacity, defined as the ability to value, assimilate, and apply external knowledge (Cohen &amp; Levinthal, 1990), helps firms identify emerging opportunities and evaluate their prospects, thus enhancing exploration. It adjusts firms’ aspiration levels, so that they become attuned to learning opportunities and more proactive in exploring them. Indeed, prior research has demonstrated how absorptive capacity enhance organizational responsiveness and directs scientific and entrepreneurial discovery (Deeds, 2001; Rosenkopf &amp; Nerkar, 2001). It also increases the likelihood of identifying external opportunities and can therefore lead to exploration in one or more domains of alliance formation.” For more on Run-Transform-Innovate see below… When I asked how he measures the performance and effectiveness of IBM&apos;s IT team, Hennessy pointed to its &amp;quot;run-to-transform&amp;quot; ratio. IBM&apos;s IT department is divided into three groups: a &amp;quot;run&amp;quot; organization that&apos;s responsible for keeping systems running smoothly; a &amp;quot;transform&amp;quot; team focused on business-process simplification and other business transformation; and an &amp;quot;innovate&amp;quot; unit that pursues leading-edge technology initiatives. Hennessy reports to Linda Sanford, IBM&apos;s senior VP of on-demand transformation and IT. Practicing what it preaches, IBM doesn&apos;t think of its IT organization as being merely an IT department. &amp;quot;We call it BT and IT,&amp;quot; Hennessy says, giving business transformation equal billing to the software, systems, and services side of its mission. http://www.informationweek.com/blog/main/archives/2009/04/ibm_cio_turns_d.html IBM CIO&apos;s Strategy: Run, Transform, Innovate Posted by John Foley on Apr 30, 2009 11:05 AM Like other CIOs, IBM&apos;s Mark Hennessy knows that a dollar saved on data center operations is a dollar earned for business-technology innovation. IBM has moved the dial on its IT budget 10 percentage points toward innovation in recent years, and Hennessy says there are still more operational efficiencies to be gained.I sat down with Hennessy for more than an hour recently in New York to talk about how he has adapted to being a CIO. A 25-year IBM veteran, he took over as CIO about 18 months ago, having spent most of his career on the business side, in sales, marketing, finance, and, most recently, as general manager of IBM&apos;s distribution sector, which works with clients in the retail, travel, transportation, and consumer products industries. Hennessy&apos;s IT team supports the company&apos;s strategy in three broad ways: by running and optimizing IBM&apos;s internal IT operations, by working with IBM business units in support of their objectives, and by facilitating company-wide collaboration, innovation, and technology requirements across 170 countries. In times past, IBM had as many as 128 different CIOs across its businesses. These days--in support of CEO Sam Palmisano&apos;s strategy of establishing a global, integrated enterprise--it has only one, and Hennessy is it. When I asked how he measures the performance and effectiveness of IBM&apos;s IT team, Hennessy pointed to its &amp;quot;run-to-transform&amp;quot; ratio. IBM&apos;s IT department is divided into three groups: a &amp;quot;run&amp;quot; organization that&apos;s responsible for keeping systems running smoothly; a &amp;quot;transform&amp;quot; team focused on business-process simplification and other business transformation; and an &amp;quot;innovate&amp;quot; unit that pursues leading-edge technology initiatives. A few years ago, IBM was spending 73% of its IT budget on keeping systems and services running and 27% on innovation. This year, its run-to-transform ratio will hit 63%-37%. Roughly speaking, IBM is shifting an additional 2% of its IT budget from run to innovation each year, and Hennessy has every expectation that his group will continue moving the ratio in that direction. &amp;quot;I don&apos;t see an end in sight,&amp;quot; he says. In fact, Hennessy says that IBM&apos;s run-to-innovation ratio has improved more this year than last. &amp;quot;So it&apos;s actually accelerating for us,&amp;quot; he says. Where do the efficiencies come from? The same place other CIOs find them. Server virtualization, data center consolidation (IBM has consolidated 155 data centers down to five), energy savings, applications simplification (from 15,000 apps to 4,500 apps), end user productivity, organizational collaboration, shifting skills globally, and business-process simplification. IBM has internal IT projects underway now in the areas of its supply chain, finance, workforce management, and order-to-cash processes. Hennessy reports to Linda Sanford, IBM&apos;s senior VP of on-demand transformation and IT. Practicing what it preaches, IBM doesn&apos;t think of its IT organization as being merely an IT department. &amp;quot;We call it BT and IT,&amp;quot; Hennessy says, giving business transformation equal billing to the software, systems, and services side of its mission.

Universities connect information flows between other HSS, cities, states, nations Local optimizations can spread quickly to other HSS… Top 3000 cities: http://www.mongabay.com/cities_pop_02.htm Of course the opportunity is not just local – while local innovation impact the lives of staff, faculty, students and their families most directly – as cities partner more (twin city and sister city programs) and as universities also establish global collaborations with campuses in other regions of the world – the opportunity for better city-university partnerships is both local and global.

Permission to re-distribute granted by Jim Spohrer – please request via email (spohrer@us.ibm.com) This talk provided a concise introduction to SSME+D evolving, and applying Service Science to build a Smarter Planet… Reference content from this presentation as: Spohrer, JC (2010) Presentation: SSME+D (for Design) Evolving: Update on Service Science Progress &amp; Directions. Event. Place. Date. Permission to redistribute granted upon request to spohrer@us.ibm.com But I want to end by sharing some relevant quotes… The first you may have seen on TV or heard on the radio – it is from IBM – Instrumented, Interconnected, Intellient – Let’s build a smarter planet (more on this one shortly) Second, If we are going to build a smarter planet, let’s start by building smarter cities, (as we will see cities turn out to be ideal building blocks to get right for a number of reasons) And if we focus on cities, then the quote from the Foundation Metropolitan paints the right picture, cities learning from cities learning from cities… The next is probably the best known quote in the group “think global, act local” (we will revisit this important thought) Since all the major cities of the world have one or more universities, the next quote is of interest “the future is born in universities” And two more well known quotes about the future – the best way to predict the future is to build it, and the future is already here… it is just not evenly distributed. The next quote is an important one for discipline specialists at universities to keep in mind – real-world problems may not respect discipline boundaries (so be on guard for myopic solutions that appear too good to be true, they often are!)… Because if we are not careful, today’s problems may come from yesterday’s solutions… And since we cannot anticipate all risks or quickly resolve them once we notice them, we should probably never forget what HG Wells said - that history is a race between education and catastrophe… In a world of accelerating change, this last statement also serves as a reminder that the pace of real innovation in education is a good target for study in terms of smarter systems and modern service…

In conclusion, let’s consider the big picture – starting with the big bang…. and evolution of the earth, life on earth, human life, cities, universities, and the modern world… the evolution of observed hierarchical-complexity Age of natural systems (age of the universe): Big Bang http://en.wikipedia.org/wiki/Age_of_the_universe Age of urban systems (age of complex human-made world): Oldest city http://en.wikipedia.org/wiki/List_of_cities_by_time_of_continuous_habitation (end of last Ice Age was about 20,000 years ago, about 5 million people on earth by 10,000 years ago) http://www.ncdc.noaa.gov/paleo/ctl/100k.html (last Ice Age was probably started about 70,000 years ago when a super volcano erupted blocking sun light) Many people still ask -- where is the science in the “Service Science?” One answer is that the science is hidden away in each of the component disciplines that study service systems, scientifically from their particular perspective… However, the big picture answer is “Ecology” - Ecology is the study of the abundance and distribution of entities (populations of things) in an environment… and how the entities interact with each other and their environment over successive generations of entities. The natural sciences (increasingly interdisciplinary) study the left side, using physics, chemistry, and biology Service science (originated as interdisciplinary) studies the right side, using history, economics, management, engineering, design, etc. Service science is still a young area, but from the growth of service in nations and businesses to the opportunity to apply service science to build a smarter planet, innovate service systems, and improve quality of life… it is an emerging science with bright future, and yes… it will continue to evolve : - ) Most people think of ecology in terms of living organisms, like plants and animals in a natural environment. However, the concept of ecology is more general and can be applied to entities as diverse as the populations of types of atoms in stars to the types of businesses in a national economy. I want to start my talk today on “service,” by first thinking broadly about ecologies of entities and their interactions. Eventually, we will get to human-made service system entities and human-made value-cocreation mechanisms… but for today, let’s really start at the very beginning – the big bang. About 14B years ago (indicated by the top of this purple bar), our universe started with a big bang. And through a process of known as fusion, stars turned populations of lighter atoms like hydrogen into heavier atoms like helium, and when stars of a certain size have done all the fusion they could, they would start slowing down, and eventually collapse rapidly, go nova, explode and send heavier atoms out into the universe, and eventually new stars form, and the process repeats over and over, creating stars with different populations of types of atoms, including heavier and heavier elments. So where did our sun and the earth come from…. Eventually after about ten billion years in the ecology of stars and atoms within stars, a very important star formed our sun (the yellow on the left) – and there were plenty of iron and nickel atoms swirling about as our sun formed, and began to burn 4.5B years ago, and the Earth formed about 4.3B years ago (the blue on the left)… In less than a billion years, the early earth evolved a remarkable ecology of complex molecules, including amino acids, and after less than a billion years, an ecology of bacteria took hold on early earth (the bright green on the left). The ecology of single cell bacteria flourished and after another billion years of interactions between the bacteria, the first multicellular organisms formed, and soon the ecology of sponges (the light blue on the left) and other multi-cellular entities began to spread out across the earth. Then after nearly two billion years, a type of division of labor between the cells in multicelluar organism lead to entities with cells acting as neurons in the first clams (the red on the left), and these neurons allowed the clams to open and close at the right time. After only 200 million years, tribolites appeared the first organisms with dense neural structures that could be called brains appeared (the black on the left), and then after about 300 million years, multicelluar organisms as complex as bees appeared (the olive on the left), and these were social insects, with division of labor among individuals in a population, with queens, drones, worker bees. So 200 million years ago, over 13B years after the big bang, the ecology of living entities is well established on planet earth, including social entities with brains and division of labor between individuals in a population…. Living in colonies that some have compared to human cities – where thousands of individuals live in close proximity and divide up the work that needs to be done to help the colony survive through many, many generations of individuals that come and go. Bees are still hear today. And their wingless cousins, called ants, have taken division of labor to incredible levels of complexity in ant cities in nearly every ecological niche on the planet, except under water. Now let’s look at the human ecology,and the formation of service system entities and value-cocreation mechanisms, a small portion of which is represented by the colored bar on the right. Recall bees appeared about 200 million years ago, a small but noticeable fraction of the age of the universe. Now take 1% of this little olive slice, which is 2 million years… that is how long people have been on earth, just one percent of this little olive slice here. What did people do in most of that 2million years? Basically, they spread out to every corner of the planet, and changed their skin color, eye colors, and hair colors, they spread out and became diverse with many different appearances and languages. It took most of that 200 millions just to spread out and cover most of the planet with people. When there was no more room to spread out the density of people in regions went up…. Now take 1% of that 2million years of human history which basically involved spreading out to every corner of the planet and becoming more diverse, recall ecology is the study of abundance and distribution and types of interactions, and 1% of that 2million years is just 20,000 years, and now divide that in half and that represents 10,000 years. The bar on the right represents 10,000 years or just 500 generations of people, if a generation is about 20 years. 500 generations ago humans built the first cities, prior to this there were no cities so the roughly 5M people spread out around the world 0% lived in cities, but about 500 generations ago the first cities formed, and division of labor and human-made service interactions based on division of labor took off – this is our human big bang – the explosion of division of labor in cities. Cities were the big bang for service scientists, because that is when the diversity of specialized roles and division of labor, which is at the heart of a knowledge-based service economy really begins to take off... So cities are the first really important type of human-made service system entities for service scientists to study, the people living in the city, the urban dwellers or citizens are both customers of and providers of service to each other, and division of labor is the first really important type of human-made value-cocreation mechanism for service scientists to study. (Note families are a very important type of service system entity, arguably more important than cities and certainly much older – however, family structure is more an evolution of primate family structure – and so in a sense is less of a human-made service system entity and more of an inherited service system entity… however, in the early cities often the trades were handed down father to son, and mother to daughter as early service businesses were often family run enterprises in which the children participated – so families specialized and the family names often reflect those specialization – for example, much later in England we get the family names like smith, mason, taylor, cooper, etc.) So to a service scientist, we are very excited about cities as important types of service system entities, and division of labor as an important type of value-cocreation mechanism, and all this really takes off in a big way just 500 generations ago when the world population was just getting to around 5M people spread out all around the world – so 10,000 years about about 1% of the worlds population was living in early versions of cities. It wasn’t until 1900 that 10% of the world’s then nearly 2B people lived in cities, and just this last decade that 50% of the worlds 6B people lived in cities, and by 2050 75% of the worlds projected 10B population will be urban dwellers. If there is a human-made service system that we need to design right, it is cities. It should be noted that the growth of what economist call the service sector, parallels almost exactly the growth of urban population size and increased division-of-labor opportunities that cities enable – so in a very real sense SERVICE GROWTH IS CITY GROWTH OR URBAN POPULATION GROWTH… in the last decade service jobs passed agriculture jobs for the first time, and urban dwellers passed rural dwellers for the first time. But I am starting to get ahead of myself, let’s look at how the human-made ecology of service system entities and value-cocreation mechanisms evolved over the last 10,000 years or 500 generations. The population of artifacts with written language on them takes off about 6000 years ago or about 300 generations ago (the yellow bar on the right). Expertise with symbols helped certain professions form – and the first computers were people writing and processing symbols - scribes were required, another division of labor – so the service of reading and writing, which had a limited market at first began to emerge to help keep better records. Scribes were in many ways the first computers, writing and reading back symbols – and could remember more and more accurately than anyone else. Written laws (blue on right) that govern human behavior in cities takes off about 5000 years ago – including laws about property rights, and punishment for crimes. Shortly there after, coins become quite common as the first type of standard monetary and weight measurement system (green on right). So legal and economic infrastructure for future service system entities come along about 5000 years ago, or 250 generations ago, with perhaps 2% of the population living in cities…. (historical footnote: Paper money notes don’t come along much until around about 1400 years ago – bank notes, so use of coins is significantly older than paper money, and paper money really required banks as service system entities before paper money could succeed.). About 50 generations ago, we get the emergence of another one of the great types of service system entities – namely universities (light blue line) – students are the customers, as well as the employers that need the students. Universities help feed the division of labor in cities that needed specialized skills, including the research discipline skills needed to deepen bodies of knowledge in particular discipline areas. The red line indicates the population of printing presses taking off in the world, and hence the number of books and newspapers. This was only about 500 years or 25 generations ago. Now university faculty and students could more easily get books, and cities began to expand as the world’s population grew, and more cities had universities as well. The black line indicates the beginning of the industrial revolution about 200 years ago, the sream engine, railroads, telegraph and proliferation of the next great type of service system entity – the manufacturing businesses - that benefited from standard parts, technological advances and scale economies, and required professional managers and engineers. About 100 years ago, universities began adding business schools to keep up with the demand for specialized business management skills, and many new engineering disciplines including civil engineering, mechanical engineering, chemical engineering, and electrical engineering, fuel specialization and division of labor. By 1900, just over 100 years ago, or 5 generations ago 10% of the worlds population, or about 200 million people were living in cities and many of those cities had universities or were starting universities. Again fueling specialization, division of labor, and the growth of service as a component of the economy measured by traditional economists. Finally, just 60 years ago or 3 generations ago, the electronic semiconductor transistor was developed (indicated by the olive colored line on the right), and the information age took off, and many information intensive service activities could now benefit from computers to improve technology (e.g., accounting) and many other areas. So to recap, cities are one of the oldest and most important type of service system and universities are an important and old type of service system, as well as many types of businesses. Service science is the study of service system entities, their abundance and distribution, and their interactions. Division of labor is one of the most important types of value cocreation mechanisms, and people often need specialized skills to fill roles in service systems. Service science like ecology studies entities and their interactions over successive generations. New types of human-made service system entities and value-cocreation mechanisms continue to form, like wikipedia and peer production systems. Age of Unvierse (Wikipedia) The age of the universe is the time elapsed between the Big Bang and the present day. Current theory and observations suggest that the universe is 13.75 ±0.17 billion years old. [1] Age of Sun The Sun was formed about 4.57 billion years ago when a hydrogen molecular cloud collapsed. [85] Solar formation is dated in two ways: the Sun&apos;s current main sequence age, determined using computer models of stellar evolution and nucleocosmochronology , is thought to be about 4.57 billion years. [86] This is in close accord with the radiometric date of the oldest Solar System material, at 4.567 billion years ago. [87] [88] Age of Earth The age of the Earth is around 4.54 billion years (4.54 × 109 years ± 1%). [1] [2] [3] This age has been determined by radiometric age dating of meteorite material and is consistent with the ages of the oldest-known terrestrial and lunar samples . The Sun , in comparison, is about 4.57 billion years old , about 30 million years older. Age of Bacteria (Uni-cellular life) The ancestors of modern bacteria were single-celled microorganisms that were the first forms of life to develop on earth, about 4 billion years ago. For about 3 billion years, all organisms were microscopic, and bacteria and archaea were the dominant forms of life. [22] [23] Although bacterial fossils exist, such as stromatolites , their lack of distinctive morphology prevents them from being used to examine the history of bacterial evolution, or to date the time of origin of a particular bacterial species. However, gene sequences can be used to reconstruct the bacterial phylogeny , and these studies indicate that bacteria diverged first from the archaeal/eukaryotic lineage. [24] The most recent common ancestor of bacteria and archaea was probably a hyperthermophile that lived about 2.5 billion–3.2 billion years ago. [25] [26] Cities (Wikipedia) Early cities developed in a number of regions of the ancient world. Mesopotamia can claim the earliest cities, particularly Eridu, Uruk, and Ur. After Mesopotamia, this culture arose in Syria and Anatolia, as shown by the city of Çatalhöyük (7500-5700BC). Writing (Wikipedia) Writing is an extension of human language across time and space. Writing most likely began as a consequence of political expansion in ancient cultures, which needed reliable means for transmitting information, maintaining financial accounts, keeping historical records, and similar activities. Around the 4th millennium BC, the complexity of trade and administration outgrew the power of memory, and writing became a more dependable method of recording and presenting transactions in a permanent form [2] . In both Mesoamerica and Ancient Egypt writing may have evolved through calendrics and a political necessity for recording historical and environmental events. Written Law (Wikipedia) The history of law is closely connected to the development of civilization . Ancient Egyptian law, dating as far back as 3000 BC, contained a civil code that was probably broken into twelve books. It was based on the concept of Ma&apos;at , characterised by tradition, rhetorical speech, social equality and impartiality. [81] [82] By the 22nd century BC, the ancient Sumerian ruler Ur- Nammu had formulated the first law code , which consisted of casuistic statements (&amp;quot;if ... then ...&amp;quot;). Around 1760 BC, King Hammurabi further developed Babylonian law , by codifying and inscribing it in stone. Hammurabi placed several copies of his law code throughout the kingdom of Babylon as stelae , for the entire public to see; this became known as the Codex Hammurabi . The most intact copy of these stelae was discovered in the 19th century by British Assyriologists, and has since been fully transliterated and translated into various languages, including English, German, and French. [83] Money (Wikipedia) Many cultures around the world eventually developed the use of commodity money . The shekel was originally both a unit of currency and a unit of weight. [10] . The first usage of the term came from Mesopotamia circa 3000 BC. Societies in the Americas, Asia, Africa and Australia used shell money – usually, the shell of the money cowry ( Cypraea moneta ) were used. According to Herodotus , and most modern scholars, the Lydians were the first people to introduce the use of gold and silver coin . [11] It is thought that these first stamped coins were minted around 650–600 BC. [12] Universities (Wikipedia) Prior to their formal establishment, many medieval universities were run for hundreds of years as Christian cathedral schools or monastic schools ( Scholae monasticae ), in which monks and nuns taught classes; evidence of these immediate forerunners of the later university at many places dates back to the 6th century AD. [7] The first universities were the University of Bologna (1088), the University of Paris (c. 1150, later associated with the Sorbonne ), the University of Oxford (1167), the University of Palencia (1208), the University of Cambridge (1209), the University of Salamanca (1218), the University of Montpellier (1220), the University of Padua (1222), the University of Naples Federico II (1224), the University of Toulouse (1229). [8] [9] Printing and Books (Wikipedia) Johannes Gutenberg&apos;s work on the printing press began in approximately 1436 when he partnered with Andreas Dritzehn—a man he had previously instructed in gem-cutting—and Andreas Heilmann, owner of a paper mill. [34] However, it was not until a 1439 lawsuit against Gutenberg that an official record exists; witnesses&apos; testimony discussed Gutenberg&apos;s types, an inventory of metals (including lead), and his type molds. [34]

In the Handbook of Service Science, and other publications, we have layed out the conceptual foundations of service science – the first approximation of terms we believe every service scientist should know… The world view is that of an ecology of service-system-entities. Ecology is the study of the populations of entities, and their interactions with each other and the environment Types of Service System Entities, Interactions, and Outcomes is what a service scientist studies. Service systems include: Person, Family/Household, Business, Citiy, Nation, University, Hospital, Call-Center, Data-Center, etc. – any legal entity that can own property and be sued We see that Resources (People, Technology, Information, Organizations) and Stakeholder (Customers, Providers, Authorities, Competitors) are part of the conceptual framework for service science.